Continuous-time event sequences (CTES) have become pervasive across many applications ranging from healthcare [1]}, traffic [2]}, [3]}, social networks [4]}, [5]}, finance [6]}, [7]}, and sensor networks [8]}, [9]}. Unlike images and text, the data quality of CTES is highly susceptible to the collection process i.e. a few missing events or shorter sequences can significantly hamper the data quality and consequently the performance of the neural models trained on such datasets. This situation is further exacerbated by privacy restrictions such as GDPR [10]} and thus, overcoming the drawbacks in dataset quality is a non-trivial task unaddressed in the past literature.
